The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations
Open accessAbstract
This paper introduces two very fast and competitive hyperspectral image (HSI) restoration algorithms: fast hyperspectral denoising (FastHyDe), a denoising algorithm able to cope with Gaussian and Poissonian noise, and fast hyperspectral inpainting (FastHyIn), an inpainting algorithm to restore HSIs where some observations from known pixels in some known bands are missing. FastHyDe and FastHyIn fully exploit extremely compact and sparse HSI representations linked with their low-rank and self-similarity characteristics. In a series of experiments with simulated and real data, the newly introduced FastHyDe and FastHyIn compete with the state-of-the-art methods, with much lower computational complexity.
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Citations
Cites
Image denoising by sparse 3-D transform-domain collaborative filtering
Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.901238 · Cited by 9,283
A Non-Local Algorithm for Image Denoising
Buades, A., Coll, B., & Morel, J.-M. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.38 · Cited by 7,616
Bertalmio, M., Sapiro, G., Caselles, V., & Ballester, C. (2000) · Proceedings of the 27th annual conference on Computer graphics and interactive techniques - SIGGRAPH '00 · doi:10.1145/344779.344972 · Cited by 3,580
Region Filling and Object Removal by Exemplar-Based Image Inpainting
Criminisi, A., Perez, P., & Toyama, K. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2004.833105 · Cited by 3,258 · cited 1×
“Exemplar-based image inpainting fills missing regions of an image by searching for similar patches in a neighboring region of the image, and copying the pixels from the most similar patch into the missing region [7], [33].”
BackgroundTexture synthesis by non-parametric sampling
Efros, A. A., & Leung, T. K. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790383 · Cited by 3,023
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Bioucas-Dias, J. M., Plaza, A., Dobigeon, N., Parente, M., Du, Q., Gader, P., & Chanussot, J. (2012) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2012.2194696 · Cited by 2,836
Self-citationSignal Recovery by Proximal Forward-Backward Splitting
Combettes, P. L., & Wajs, V. R. (2005) · Multiscale Modeling & Simulation · doi:10.1137/050626090 · Cited by 2,678
Vertex component analysis: a fast algorithm to unmix hyperspectral data
Nascimento, J. M. P., & Dias, J. M. B. (2005) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2005.844293 · Cited by 2,669
Keshava, N., & Mustard, J. F. (2002) · IEEE Signal Processing Magazine · doi:10.1109/79.974727 · Cited by 2,549
Recent advances in techniques for hyperspectral image processing
Plaza, A., Benediktsson, J. A., Boardman, J. W., Brazile, J., Bruzzone, L., Camps-Valls, G., Chanussot, J., Fauvel, M., Gamba, P., Gualtieri, A., et al. (2009) · Remote Sensing of Environment · doi:10.1016/j.rse.2007.07.028 · Cited by 1,687
Metadata
- Authors
- Zhuang, L., & Bioucas-Dias, J. M.
- Venue
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Published
- 2018-03
- Volume
- 11
- Issue
- 3
- Pages
- 730-742
- ISSN
- 1939-1404 (ISSN-L)
- 2151-1535
- OpenAlex
- W2790888198
- Cited by
- 363
- References
- 43
- FWCI
- 17.57
- Type
- journal article
- Language
- en
- Open access
- green
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- Zhuang, L., & Bioucas-Dias, J. M. (2018). Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/jstars.2018.2796570
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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